Prediction of sheep milk chemical composition using ph, electrical conductivity and refractive index

Transcription

1 Prediction of sheep milk chemical composition using ph, electrical conductivity and refractive index A.I. Gelasakis *, R. Giannakou *, A. Kominakis, G. Antonakos, G. Arsenos * * Department of Animal Production, Faculty of Veterinary Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece Department of Animal Breeding and Husbandry, Faculty of Animal Science and Aquaculture, Agricultural University of Athens, Athens, Greece Agricultural Cooperative of Western Greece, Agrinio, Aitoloakarnania, Greece

2 Milk quality traits Milk quality traits (fat, protein, lactose and total solids content) - Directly linked to the processing performance of sheep milk Improvement of milk quality traits Dairy industry s demand Milk quality traits are expensive and difficult to measure - Need laboratories with trained staff - Normally done by organized farmers networks and cooperatives - Logistics for the transferring of samples - Considerable capital investment 2

3 The situation in Greece Farmers in Greece - Do not have easy access to these labs - Do not consider breeding for milk quality because it is expensive and difficult to measure The idea Can we use other physical traits to select for milk quality? Other trait might include ph, milk electrical conductivity, refractive index Utilize 1) low-cost, 2) readily-used and 3) suitable for on-farm use, analyzers 3

4 Objective To assess if ph, electrical conductivity and refractive index of sheep milk can predict its fat, protein, lactose and total solids content 4

5 Animals and measurements 308 Frizarta ewes from two semi-intensive flocks Composite milk samples in 50 ml capacity tubes Milk electrical conductivity (MEC) and ph were measured (ph/conductivity meter (EZDO 7200, GMM Technoworld ) Refractive index (RI) was measured with a portable Brix (0-25 Bx) (RHW-25ATC-BE, Hong Han Technologies) Milk quality traits were measured using MilkoScan TM, Foss 5

6 Statistics SPSS Multiple stepwise regression analysis predicted variable = intercept + ph + MEC + RI + e the predicted variables were milk quality traits (fat, protein, lactose and total solids content) e = random error 6

7 Assumptions testing passed Assumptions of normality, linearity and homoscedasticity: - Plots of standardized residuals against standardized predicted values - Probability-probability plots (p-p plots) of regression standardized residuals - Variance inflation factor (VIF) was calculated for each predictor to ensure there was no violation of the assumption of multicollinearity 7

8 Plots of standardized residuals against standardized predicted values

9 Probability-probability plots (p-p plots) of regression standardized residuals

10 Validation Prediction models were validated using: - Bootstrapping - 1,000 bootstrap samples were repeatedly resampled, with replacement - Split-half cross validation - Regression coefficients, standard errors, 95% confidence intervals and p- values for the overall model and for the predictors were calculated - Statistical significance was set at P

11 Descriptives Physical properties of sheep milk and milk quality traits Variable Mean Std. Dev. ph Conductivity (ms/cm) Refractive index ( Bx) Fat content (%) Protein content (%) Lactose content (%) Total solids content (%)

12 Milk fat Coefficients Unstandardized coefficients Collinearity statistics Predictor Β S.E. P VIF * Intercept Refractive index ph Bootstrap for Coefficients Predictor Β S.E. P Intercept Refractive index ph

13 Milk protein Coefficients Unstandardized coefficients Collinearity statistics Predictor Β S.E. P VIF * Intercept Refractive index Conductivity Bootstrap for Coefficients Predictor Β S.E. P Intercept Refractive index Conductivity

14 Milk lactose Coefficients Unstandardized coefficients Collinearity statistics Predictor Β S.E. P VIF * Intercept Refractive index Conductivity Bootstrap for Coefficients Predictor Β S.E. P Intercept Refractive index Conductivity

15 Milk total solids Coefficients Unstandardized coefficients Collinearity statistics Predictor Β S.E. P VIF * Intercept Refractive index Conductivity ph Bootstrap for Coefficients Predictor Β S.E. P Intercept Refractive index Conductivity ph

16 Split-half cross validation Full data set Split 1 Split 2 ANOVA significance (P 0.05) Fat R Protein Significant coefficients in the model* RI *, ph RI, ph RI ANOVA significance (P 0.05) R Significant coefficients in the model RI, MEC ** RI, MEC RI, MEC Lactose Total solids ANOVA significance (P 0.05) R Significant coefficients in the model RI, MEC RI, MEC RI, MEC ANOVA significance (P 0.05) R Significant coefficients in the model RI, MEC, ph RI, MEC, ph RI, MEC *P

17 Results Prediction equations of milk quality traits: Fat = RI ph Protein = RI MEC Lactose = RI MEC Total solids = RI MEC ph Example A milk sample with Refractive index = 14.0 Bx ph = 6.7 Electrical conductivity = 4.0 ms/cm is expected to have Fat = 6.0 % Protein = 5.8 % Lactose = 4.6 % Total Solids = 17.2 % 17

18 Conclusions We can predict milk protein, lactose and total solids content using refractive index and milk electrical conductivity in sheep s milk Milk quality trait prediction models will help farmers and breeders breed the sheep that produce the best quality milk External validation of the models using milk produced from different a) dairy sheep breeds b) farming systems c) time-points within the lactation will make the predictions more applicable 18

19 Acknowledgements The Laboratory of Milk Quality Control of the Hellenic Milk and Meat Organization (ELOGAK, Epirus Department) Vasilios Konstantinou and the farmers Ioannis Tolis and Panagiotis Peslis from the Agricultural Cooperative of Western Greece are greatly acknowledged for their collaboration and contribution to the study 19

20 Thank you for your attention 20

21 Conclusions We can predict milk protein, lactose and total solids content using refractive index and milk electrical conductivity in sheep s milk Milk quality trait prediction models will help farmers and breeders breed the sheep that produce the best quality milk External validation of the models using milk produced from different a) dairy sheep breeds b) farming systems c) time-points within the lactation will make the predictions more applicable 21